Running Enterprise Agents in Production: Architecture and Secure Execution Models

I lead large-scale AI implementations and design agents for some of the most security-sensitive enterprises in the world. The pattern is always the same: prototypes and MVPs are easy, running agents in production is not. The moment an agent touches real data and real systems, security, policy, identity, orchestration, and auditability dominate the design. You also discover how many teams must align to make it safe. Without a shared and evolving playbook, the deployment stalls. In this talk I outline the approach I use to build enterprise agents that actually run in production. I cover the core architecture, the supervisory runtime, and the secure execution model that keeps agents predictable: identity boundaries, retrieval controls, RBAC and ABAC alignment, sandboxed tools, and evaluation loops. Through this talk I'd like to show what it really takes to operate agents inside large organizations and provide a practical blueprint you can adapt to your own environment. The views, opinions, and content shared in this presentation are my own and do not represent those of any employer, past or present. I’d also like to note that my book, Data Engineering for Multimodal AI (O’Reilly), is authored independently and is neutral in perspective. It is not sponsored by Salesforce and does not represent Salesforce’s viewpoints.